Development of a Machine-Learning Model of Short-Term Prognostic Prediction for Spinal Stenosis Surgery in Korean Patients.
Kyeong-Rae KimHyeun-Sung KimJae-Eun ParkSeung-Yeon KangSo-Young LimIl-Tae JangPublished in: Brain sciences (2020)
This study verified that the individual characteristics of the patient and treatment characteristics during surgery enable a prediction of the patient prognosis and validate the accuracy of the approach. Further studies should be conducted to extend the scope of this research by incorporating a larger and more accurate dataset.
Keyphrases
- minimally invasive
- machine learning
- end stage renal disease
- coronary artery bypass
- case report
- ejection fraction
- chronic kidney disease
- newly diagnosed
- spinal cord
- peritoneal dialysis
- prognostic factors
- surgical site infection
- artificial intelligence
- high resolution
- patient reported outcomes
- coronary artery disease